Observer design applied to prognosis of system

Abstract

This paper is dedicated to model-based prognosis to predict remaining useful life of a system. This methodology is applied on multiple time scale systems made up of a slow and a fast dynamic behaviors subsystems, defining damage state and state of system behaviors respectively. Prediction of remaining useful life implies to have a slow dynamic behavior subsystem model. Slow dynamic subsystem behavior is supposed to be unknown, only the structure is assumed to be known a priori. For that, in the fast dynamic behavior subsystem, unmeasured state are estimated based on the design of unknown input observers. Slow dynamic behavior state in the fast dynamic behavior subsystem is led back to an unknown input. High gain observer is used to obtain accurate state and unknown input estimates. Slow dynamic behavior model parameters are then identified with the previous estimates. Prediction of remaining useful life is finally achieved based on relative accuracy of the observer estimates. Pertinence of the proposed methodology is illustrated based on simulation results to an electromechanical oscillator.Index Terms-Prognosis, predictive diagnosis, unknown input observer, electromechanical system.

A Comparative Study of Unknown-Input Observers for Prognosis Applied to an Electromechanical System

“…For , let denote the solution of the system defined on by (12) Let and be the estimates of and , respectively, obtained by using an observer for (10). The estimation of the parameter can be done by solving the following optimization problem:…”

“…In a third approach, unknown input observers allow both state and unknown input estimates simultaneously (see [10] and [19]). This kind of approach is suitable for our methodology design and has been used in previous studies (see [12] and [13]). Two types of nonlinear observers are considered: high gain observers and sliding mode observers.…”

A generic ageing model for prognosis - Application to Permanent Magnet Synchronous Machines

“…The ageing model can be an analytical model, represented as a set of equations which involve physical quantities corresponding to environmental constraints (Onori, Rizzoni, & Cordoba-Arenas, 2012;Bregon, Daigle, & Roychoudhury, 2012), or a simulation model identified from tests results. In (Gucik-Derigny, Outbib, & Ouladsine, 2011), the ageing model is represented as a set of nonlinear differential equations with multiple time scales (short for the system behavior dynamic and large for its degradation). The fast dynamic state is estimated thanks to observers and the parameters of the ageing model (i.e.…”